Food Insecurity Knowledge and Training Among College Students in Health Majors
Bibliographic record
Abstract
OBJECTIVE: To describe current food insecurity (FI)-related training among nutrition/dietetics, public health, and social work students. METHODS: A cross-sectional online survey was used among students (n = 306) enrolled in health-related programs at 12 US universities. Participants reported FI-related course-based and extracurricular experiences and rated confidence to address FI on a scale of 1-3. Open-ended questions investigated perceived definitions of FI and impactful course activities. Descriptive statistics and thematic analysis were used for data analysis. RESULTS: Participants' FI definitions were multifaceted. Most (80.6%) reported FI being covered in at least 1 course. The overall mean confidence to address FI was 2.2 ± 0.48. Participants suggested increasing application-based opportunities and skills training. CONCLUSIONS AND IMPLICATIONS: Most students have a basic understanding of FI and report high confidence to address it in the future. Impactful FI-related experiences and participants' suggestions guide developing an FI training resource to enhance student FI competency and sensitivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".